EnricoMi commented on code in PR #36150:
URL: https://github.com/apache/spark/pull/36150#discussion_r881695872
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sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -2012,6 +2012,152 @@ class Dataset[T] private[sql](
@scala.annotation.varargs
def agg(expr: Column, exprs: Column*): DataFrame = groupBy().agg(expr, exprs
: _*)
+ /**
+ * (Scala-specific)
+ * Unpivot a DataFrame from wide format to long format, optionally
+ * leaving identifier variables set.
+ *
+ * This function is useful to massage a DataFrame into a format where some
+ * columns are identifier variables (`ids`), while all other columns,
+ * considered measured variables (`values`), are "unpivoted" to the rows,
+ * leaving just two non-identifier columns, 'variable' and 'value'.
+ *
+ * {{{
+ * val df = Seq((1, 11, 12L), (2, 21, 22L)).toDF("id", "int", "long")
+ * df.show()
+ * // output:
+ * // +---+---+----+
+ * // | id|int|long|
+ * // +---+---+----+
+ * // | 1| 11| 12|
+ * // | 2| 21| 22|
+ * // +---+---+----+
+ *
+ * df.melt(Seq("id")).show()
+ * // output:
+ * // +---+--------+-----+
+ * // | id|variable|value|
Review Comment:
The `variable` and `value` column names come from the [Pandas
API](https://pandas.pydata.org/docs/reference/api/pandas.melt.html). So this is
identical to existing [PySpark Pandas
API](https://github.com/apache/spark/blob/v3.2.1/python/pyspark/pandas/frame.py#L9019-L9023).
SQL based DBMSes require those column names to be provided, so there is no
default. See
[BigQuery](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#unpivot_operator),
[T-SQL](https://docs.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot?view=sql-server-ver15#unpivot-example),
[Oracle](https://www.oracletutorial.com/oracle-basics/oracle-unpivot/).
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